Lead Analytics Engineer - Data Modeling & Quality
Job Description
Arcadia's data platform powers population health analytics for health plans, ACOs, and provider groups across the country. As a Lead Analytics Engineer — Data Modeling & Quality, you sit at the intersection of data quality ownership and analytical data modeling. You'll own the SQL and DBT layer that transforms raw clinical and claims data into trusted, production-grade datasets, while also serving as the quality authority for the data those models produce.
This is a hybrid role — deeper SQL and DBT expertise than a traditional Data Health Professional, with a more analytical and model-focused scope than a Data Engineering role. You're less focused on pipeline infrastructure and more on the logic, shape, and trustworthiness of the data itself.
- Independently triage and resolve pipeline data quality issues
- Author at least one new DBT model or refactor an existing one to meet current modeling standards
- Design a DBT test suite for a set of models lacking coverage
- Understand the end-to-end pipeline from ingress through silver and gold, and be able to trace a data quality issue to its root layer
- Building strong working relationships with clients and cross-functional partners (Data Engineering, Customer Success)
- Deeply familiar with Arcadia's full data stack — from ingress through silver, gold, and downstream consumers
- Driving at least one improvement project forward, whether technical (e.g. model refactor, new DQ framework) or process-focused (e.g. promotion playbook, triage workflow)
- Recognized as a leader within the department — peers and stakeholders seek out your expertise on data modeling and quality
- Operating independently across the full scope of the role with minimal guidance
- Two or more improvement projects completed and in production, with measurable impact on data quality or operational efficiency
Arcadia's data platform powers population health analytics for health plans, ACOs, and provider groups across the country. As a Lead Analytics Engineer — Data Modeling & Quality, you sit at the intersection of data quality ownership and analytical data modeling. You'll own the SQL and DBT layer that transforms raw clinical and claims data into trusted, production-grade datasets, while also serving as the quality authority for the data those models produce.
This is a hybrid role — deeper SQL and DBT expertise than a traditional Data Health Professional, with a more analytical and model-focused scope than a Data Engineering role. You're less focused on pipeline infrastructure and more on the logic, shape, and trustworthiness of the data itself.
- Independently triage and resolve pipeline data quality issues
- Author at least one new DBT model or refactor an existing one to meet current modeling standards
- Design a DBT test suite for a set of models lacking coverage
- Understand the end-to-end pipeline from ingress through silver and gold, and be able to trace a data quality issue to its root layer
- Building strong working relationships with clients and cross-functional partners (Data Engineering, Customer Success)
- Deeply familiar with Arcadia's full data stack — from ingress through silver, gold, and downstream consumers
- Driving at least one improvement project forward, whether technical (e.g. model refactor, new DQ framework) or process-focused (e.g. promotion playbook, triage workflow)
- Recognized as a leader within the department — peers and stakeholders seek out your expertise on data modeling and quality
- Operating independently across the full scope of the role with minimal guidance
- Two or more improvement projects completed and in production, with measurable impact on data quality or operational efficiency